Mathematics· 2026Q2
A Voxel-Based Adaptive Exposure Model for Physically Based Volume Rendering
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- Q2SCImago
- 2026year
Short summary
A new voxel-contribution-based adaptive exposure model for volume rendering estimates global exposure coefficients from 3D volume integration data, achieving 0.005% mean deviation compared to 0.031% for 2D methods.
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Key points
- Proposes a voxel-contribution-based adaptive exposure model for physically based volume rendering.
- Estimates global exposure coefficients using accumulated transmittance and opacity from 3D volume integration.
- Achieves a mean exposure-coefficient deviation of approximately 0.005%, significantly better than 2D methods (0.031%).
- Maintains output mean-luminance variation below 1.10% across tested illumination conditions.
AI-generated from the title and abstract; the full text is not read.
Abstract
In physically based volume rendering of volumetric medical imaging data, exposure levels are typically set empirically. However, differences in voxel values and opacity across transfer functions can make a fixed setting produce underexposed or overexposed results. To address this issue, we propose a voxel-contribution-based adaptive exposure model for physically based volume rendering. During volume integration, the method visibility-weights effective volumetric-sample energies using accumulated transmittance and opacity and estimates a global exposure coefficient from their weighted geometric mean. Compared with a two-dimensional HDR method operating on the rendered image, the proposed method incorporates three-dimensional information from volume integration into exposure estimation. The mean exposure-coefficient deviation is approximately 0.005%, compared with 0.031% for the two-dimensional method, while output mean-luminance variation remains below 1.10% over the tested range of normal illumination. The results demonstrate stable exposure and structural response across the evaluated transfer functions and illumination conditions.
The authors' abstract, as published at the source. Mathematics, 2026 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science